Coupling Process Control Systems and Process Analytics to Improve Batch Operations Bob Wojewodka, Technology Manager Philippe Moro, Global IS Manager Terry Blevins, Principal Technologist

[File Name or Event] Emerson Confidential 27-Jun-01, Slide 2 2 PresentersPresenters Robert Wojewodka Philippe Moro Terry Blevins

[File Name or Event] Emerson Confidential 27-Jun-01, Slide 3 3 IntroductionIntroduction What we will cover: –The need to move beyond process control to process data analytics coupled with control –Why this is important –Basic concepts on the analysis methodology –The Lubrizol <> Emerson alliance and collaborative work to advance these concepts –The beta test field trials

[File Name or Event] Emerson Confidential 27-Jun-01, Slide 4 4 A Premier Specialty Chemical Company Building on our “special chemistry,” a unique blend of people, processes and products, Lubrizol: Provides innovative technology to global transportation, industrial and consumer markets Pursues our growth vision to become one of the largest and most profitable specialty chemical companies in the world A special chemistry aligned for financial success The Lubrizol Corporation

[File Name or Event] Emerson Confidential 27-Jun-01, Slide 5 5 Predominantly batch Some continuous Full spectrum of automation Diversity in control systems Both reaction chemistry & blending On-line and off-line measurement systems Production in Lubrizol

[File Name or Event] Emerson Confidential 27-Jun-01, Slide 6 6 Production Challenges Addressing the required batch data structures Better addressing process relationships Characterizing process relationships sooner Identifying abnormal situations / events sooner Better relating process relationships to end process quality and economic parameters Moving process data analytics “on-line” Continual improvement of Operational Excellence

[File Name or Event] Emerson Confidential 27-Jun-01, Slide 7 7 Alliance agreement Pricing Conversion to DeltaV / many projects Standardize on aspects of PlantWeb architecture Collaboration –Exchanging process optimization and data analysis, and integration knowledge with Emerson –Emerson sharing knowledge with Lubrizol –Collaborative development projects –Lubrizol committed to assist with field trials and be early adopters Lubrizol / Emerson Alliance

[File Name or Event] Emerson Confidential 27-Jun-01, Slide 8 8 The Power of Information Raw Data Standard Reports Descriptive Modeling Predictive Modeling DataInformationKnowledgeIntelligence Optimization What happened? Why did it happen? What will happen? What is the Best that could happen? $$$ ROI $$$ ROI Adapted by RAWO from slide courtesy of SAS Inst. 2 Ad hoc Reports & OLAP Analytics Drive the Power of Information

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Levels of Analytics Identified In Lubrizol Routine & data access Off-Line On-Line Data Analytics Clients Applications Routine analyses Routine reports Routine graphical summaries Routine metrics & KPIs Vehicle for data selection by user Vehicle to deliver data to the user On-line visualization Add hoc analyses Model development Process studies Lab studies Business studies Troubleshooting Process improvement Interactive analyses …etc. People do their own analyses using the analysis tools Real-time analytics Deployment of models ASP analytics Process analytics Monitoring, feedback, control, alerts Link back into PlantWeb Web interface for the display Etc. Clients Via a Web Page

[File Name or Event] Emerson Confidential 27-Jun-01, Slide The Challenge of Batch Operation The wide range of operating conditions present challenges in the design, commissioning, and on-going manufacturing.

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Operators work in a highly complex, highly correlated and dynamic environment each day. Any help with advanced warning of pending events is valuable. Operators manage a large amount of data and information on a running unit. Even with automated units, only so much can be monitored and managed at one time. Any help with automatic monitoring across many variables is valuable. The ultimate goal is to prevent the undesirable effects of an abnormal situation by early detection or the detection of a precursor to an undesirable event. The setting:

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Each batch has data vectors Time Batches Y - Space On-line Process Measurements Quality Measurements X - Space Batch 1 Batch 2 Batch 3 Batch 4 Batch 5 Batch 6 Batch.. b i Batches all have variable length time durations The Nature Of Batch Data is also a challenge

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Y - Space On-line Process Measurements Quality Measurements X - Space Batch 1 Batch 2 Batch 3 Batch 4 Batch 5 Batch 6 Batch.. b i Operation 1, Phases 1 to p i Operation 2, Phases 1 to p i Operation 3, Phases 1 to p i Operation O i, Phases 1 to p i Within & between batch analysis WithinBatchVariation BetweenBatchVariation Analyze data in order to: Decrease costs Reduce time cycle Reduce processing problems Increase yield Improve quality Reduce waste Reduce variability Improve reliability Avoid undesirable upsets The Nature Of Batch Data is also a challenge

[File Name or Event] Emerson Confidential 27-Jun-01, Slide What is needed Process analysis (off-line, on-line), identify: –Process relationships –Influential parameters –Correlation to quality parameters –Correlation with economic parameters Alarming for operators and focused advise Process assessment and control –Monitoring process performance –Detection of upsets –Finding assignable causes –Early detection –Drill down for explanation of deviations –Actions taken and feedback

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Also a need to move beyond univariate thinking Univariate SPC Charts

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Variable 1 Variable 2 The need to move beyond univariate thinking

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Multivariate SPC Chart The need to move beyond univariate thinking

[File Name or Event] Emerson Confidential 27-Jun-01, Slide But, there are many more than 2 process variables X1X1 X2X2 X3X3 Even though in 3- dimensions, this relationship made up of X1, X2 and X3 is the one that “explains” the highest amount of the variation between the batches Principal Component #1 Principal Component #2 Measurements across many batches for these three variables. We now have a “swarm” of points in three dimensions. Measurements across many batches for these three variables. We now have a “swarm” of points in three dimensions. This relationship of X1, X2, and X3 “explains” the next highest amount of the variation between batches

[File Name or Event] Emerson Confidential 27-Jun-01, Slide The observations may be “projected” onto a plane. Principal Component 1 Principal Component 2 PC1 PC2 The analysis extends beyond 3 variables to k variables (k dimensional space) This then allows us to simplify these complex process relationships to a much lower dimension that we can use, interpret, and exploit. The analysis reduces this complexity

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Extending the analysis to correlate process relationships with outputs (the Y-space) X1X1 X2X2 X3X3 Variability Factor 1 Variability Factor 2 Y1Y1 Y2Y2 Y3Y3 Variability Factor 1 Variability Factor 2 Each observation in the x- space corresponds to a measured result in the y-space Multivariate process relationships defined (PCA) Final batch quality and output relationships defined (PLS & PLS-DA) Final batch quality and output relationships defined (PLS & PLS-DA)

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Batch Analytic Challenges  Linking data with the product identifier  Operation / phase / state data from the unit  Many sources of data need to be combined, not all is within DeltaV  Dimensionality (large in both the x-space & y-space)  Collinearity and autocorrelation  Noise and missing data  Multivariate relationships are prevalent  Addressing process dynamics  Having access to historical data  On-line requirements and on-line challenges  The need for dynamic time warping  Etc.

[File Name or Event] Emerson Confidential 27-Jun-01, Slide We Feel We Have a Solution Lubrizol has expertise and a long standing use of multivariate data analysis in support of off-line process characterization and process improvement activities. Emerson Process Management also established a research project at University of Texas (UT), Austin in September, 2005 to investigate advanced process analytics. –The primary objective of this project is to explore the on-line application of Analytics for prediction and fault detection and identification in batch operations. –Emerson’s research grant given to UT is funding the work of a PhD graduate student, Yang Zhang, under the supervision of Professor Tom Edgar. Through the Lubrizol<>Emerson alliance, we are leveraging these areas of expertise to bring the on-line analytics to a reality. Preparing For Field Trials

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Summary of Research There are many texts available on these topics Also reference chapter 8 of the book “New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits”

[File Name or Event] Emerson Confidential 27-Jun-01, Slide PCA – Principal Components Analysis –Provides a concise overview of a data set. It is powerful for recognizing patterns in data: outliers, trends, groups, relationships, etc. PLS – Projections to Latent Structures –The aim is to establish relationships between input and output variables and developing predictive models of a process. Also used to help put “atypical” process variation into a context of what is important. PLS-DA – PLS with Discriminant Analysis –When coupled, is powerful for classification. The aim is to create predictive models of the process but where one can accurately classify future unknown samples The Multivariate Analyses Being Used

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Are these methods new? NO, they have been around and in use for quite some time These are proven methods for use for characterizing process relationships They are just new to some areas such as: Pharmaceutical companies with their PAT initiatives New to many long standing industries such as the process automation and control and many others New to certain fields of study such as engineering, process engineering, control engineering, etc. Used very effectively in “off-line” process improvement studies Historically many limitations to move these methods “on-line”. But times have changed, we are now ready!

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Data Transfer via XML Pro+.net Web services Batch Exec. Consumption SAP Data Historian Operator interface Data Transfer Analysis servers Analysts Chemists Engineers Embedded Analysis XML Recipe + Schedule For this to work, there needs to be a standard architecture

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Firewall Lubrizol Windows Service Microsoft Internet Information Service Server Lubrizol Web Service XML A strong level of security to protect the flow of data Lubrizol Windows Service Microsoft Internet Information Service Server Lubrizol Web Service Lubrizol Domain Delta V Domain

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Firewall DeltaV Systems Lab Data -Device -Manual SQL Batch Historian data Makes the marriage (matrix) between Batch/Tags/Lab Utilizes Web services Organizes this for easy access from web client Web Client Tags Continuous Historian data Integration with data analytics Analysis servers

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Analytics for Batch Processes

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Model Development – Aligning Batches Data for different length of Batches is aligned using dynamic time warping The aligned data is processed using hybrid unfolding before using this to train the multi-way PCA or batch-wise unfolding for PLS/PLS-DA model.

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Support for Process Analytics History Collection of Lab and Spectral Analyzer Data Controller Module Lab Results Analytic Block DeltaV Historian Operator Station ProPlus Off- line Modeling Other Data Processing of Sample Data for Use in Analytics

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Operator Interface for Beta Test PCA – Fault Detection PLS – Quality Parameter Prediction Contribution Plot

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Planned Beta Installation Demonstrate on-line prediction of quality and economic parameters Evaluate different means of on-line fault detection and identification i.e. multi-way PCA/PLS. Show value of high fidelity process models for testing fault detection and alternate control strategies. Discuss and explore extension of the methodologies into other aspects of the process unit data Refine the approach, user interfaces, and integration with other systems

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Beta Installation

[File Name or Event] Emerson Confidential 27-Jun-01, Slide SummarySummary Fieldbus Provides Infrastructure for Improved Diagnostic Capability Improvements Require New Capability in Field Devices and DCS Integration of These Features Multivariate statistical analysis methodology is needed to correlate relationships within and between devices with that of operational data Multivariate statistical analysis methodology is needed to correlate this with product quality and other parameters of interest The analyses need to be coupled with DeltaV The analyses need to be available for both off-line process studies as well as on-line process diagnostics and control Its Time to Implement!

[File Name or Event] Emerson Confidential 27-Jun-01, Slide Where To Get More Information Many excellent texts on multivariate analysis –Also reference: “New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits” Bob Wojewodka: Philippe Moro: Terry Blevins: At the 2008 Emerson User’s Conference –We will be presenting an overview of the field trials What was done What was found What were the benefits What are the next steps